Identification and Measurement of Self-Technical Debt in Deep Learning Frameworks: A Systematic Review

• Autores: Cuatecontzi Cuahutle, Elizabeth, Medina Barrera, María Guadalupe, Cortés Maldonado, Raúl, Bueno Avendaño, Carlos Eduardo
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/287
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/287; https://doi.org/10.48168/innosoft.s24.a287; https://purl.org/42411/s24/a287; https://n2t.net/ark:/42411/s24/a287
• Nivel de acceso: acceso abierto
• Materia: Deep learning; deep learning tools; technical debt; types of technical debt; technical debt measurement; Aprendizaje profundo; deuda técnica; herramientas de aprendizaje profundo; medición de la deuda técnica; tipos de deuda técnica

Technical Debt in software development refers to the consequences of decisions prioritizing quick solutions over optimal ones. This concept, introduced by Ward Cunningham in 1992, has been widely studied to improve software quality. In the context of deep learning, Technical Debt is also present due to the use of tools that, while facilitating model creation, may generate debt and negatively impact performance. Through a three-phase process, this study presents a systematic literature review to identify the types of Technical Debt found in deep learning tools and the techniques used for its identification and measurement. The reviewed studies show that Technical Debt can arise in various development phases, such as design, requirements definition, testing, documentation, source code, algorithms, and compatibility. Other affected aspects include data, models, knowledge, and infrastructure. Several approaches have been used to identify technical debt, such as analyzing comments in static code, pull requests, and commits, applying manual techniques, text mining, neural networks, and natural language processing algorithms. In terms of measurement, statistical methods are predominantly used. The findings of this review provide a better understanding of how Technical Debt impacts deep learning tools and offer a foundation for guiding future research on its management and mitigation in the development of systems within intelligent environments.